{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from scipy import interpolate\n",
    "import numpy as np\n",
    "import arcpy\n",
    "import pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = arcpy.da.FeatureClassToNumPyArray(\"../data/weather.shp\",\n",
    "                                         [\"SHAPE@X\",\"SHAPE@Y\",\"max\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd = pandas.DataFrame(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SHAPE@X</th>\n",
       "      <th>SHAPE@Y</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.290045e+07</td>\n",
       "      <td>4.704248e+06</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.283901e+07</td>\n",
       "      <td>4.635198e+06</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.285633e+07</td>\n",
       "      <td>4.613838e+06</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.295710e+07</td>\n",
       "      <td>4.855773e+06</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.307018e+07</td>\n",
       "      <td>4.810230e+06</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        SHAPE@X       SHAPE@Y  max\n",
       "0  1.290045e+07  4.704248e+06   15\n",
       "1  1.283901e+07  4.635198e+06   14\n",
       "2  1.285633e+07  4.613838e+06   13\n",
       "3  1.295710e+07  4.855773e+06   14\n",
       "4  1.307018e+07  4.810230e+06   15"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd = pd.sort_values(by =\"SHAPE@X\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.array(pd[\"SHAPE@X\"].tolist())\n",
    "y = np.array(pd[\"max\"].tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "170"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.arange(0,170)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "170"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Program Files\\ArcGIS\\Pro\\bin\\Python\\envs\\arcgispro-py3\\lib\\site-packages\\ipykernel_launcher.py:1: DeprecationWarning: `spline` is deprecated!\n",
      "spline is deprecated in scipy 0.19.0, use Bspline class instead.\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n",
      "D:\\Program Files\\ArcGIS\\Pro\\bin\\Python\\envs\\arcgispro-py3\\lib\\site-packages\\ipykernel_launcher.py:2: DeprecationWarning: `spline` is deprecated!\n",
      "spline is deprecated in scipy 0.19.0, use Bspline class instead.\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "f = interpolate.spline(x,y,np.linspace(10, 50, 400))\n",
    "f2 = interpolate.spline(x,y,np.linspace(10, 50, 400),order=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15.99434742493608"
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f[349]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(18, 6))\n",
    "for i in range(10,70,10):\n",
    "    ax.scatter(i+339,int(f[i+339]))\n",
    "ax.plot(range(350,len(f)), f[350:len(f)], label= \"cubic\")\n",
    "ax.plot(range(350,len(f2)), f2[350:len(f2)], label= \"cubic\")\n",
    "ax.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
